Initiate the data analysis process by loading the datasets. This step is crucial for setting the stage for all the analysis work that follows.
# Load necessary libraries
suppressPackageStartupMessages(library(tidyverse))
# Set the path to the CSV file
file_path <- "Human Development Index - Full.csv"
# Read the CSV file into a dataframe
hdi_data <- read.csv(file_path, header = TRUE, stringsAsFactors = FALSE)
# Examine the structure of the dataframe
str(hdi_data)
We start by loading the tidyverse library, which is a
collection of R packages designed for data science.
We then set the path to the CSV file.
The read.csv() function is used to load the data into an R
dataframe named hdi_data. We set stringsAsFactors =
FALSE to ensure that string data is read in as character types
instead of factors, which is usually more convenient for data cleaning.
The str() function provides a detailed structure of the
dataframe, including the type of each column and the first few entries.
#Creating Interactive Table
library(DT)
datatable(hdi_data)
We first check if the DT package is installed and install
it if it’s not already available.
We load the DT library to use the datatable()
function.
The datatable() function creates an interactive table that
allows for searching, filtering, and sorting within the R environment,
typically within an R Markdown document or a Shiny app.
In this step, we’ll assess the missing values in our dataset and decide on a strategy for dealing with them. Strategies can include:
# Install and load necessary package
if (!requireNamespace("naniar", quietly = TRUE)) {
install.packages("naniar")
}
# Define a threshold for maximum allowable percentage of missing data per column
threshold <- 0.5 # 50% threshold
# Remove columns with missing data above the threshold
hdi_data <- hdi_data %>%
select_if(~ mean(is.na(.)) < threshold)
# Function to calculate the mode for categorical data
getmode <- function(v) {
uniqv <- unique(na.omit(v))
uniqv[which.max(tabulate(match(v, uniqv)))]
}
# Impute missing values for numerical and categorical data
hdi_data <- hdi_data %>%
mutate(across(where(is.numeric), ~ ifelse(is.na(.), mean(., na.rm = TRUE), .))) %>%
mutate(across(where(is.character), ~ ifelse(is.na(.), getmode(.), .)))
# Check the structure after handling missing values
str(hdi_data)
We use naniar for visualizing missing data.
We define a variable threshold that specifies the maximum
proportion of missing values allowed in a column for it to be retained.
We filter out columns where the proportion of missing values exceeds the
threshold using select_if.
We create a function getmode to compute the mode of a
vector, which is used for imputing missing categorical data.
We impute missing values in numerical columns with the column mean and
in categorical columns with the column mode using mutate
and across.
Finally, we check the structure of our data with str to
ensure that all columns have the appropriate data types and that missing
values have been handled.
This phase focuses on the crucial aspect of converting data to the correct type to ensure accurate analysis.
# Ensure correct data types for each column
# For numerical columns - convert factors or characters to numeric if necessary
hdi_data <- hdi_data %>%
mutate(across(where(is.character), ~ as.numeric(as.character(.)), .names = "numeric_{.col}"))
# For categorical columns - convert characters to factors if necessary
hdi_data <- hdi_data %>%
mutate(across(where(is.character), as.factor, .names = "factor_{.col}"))
# For date columns - convert characters to Date objects if necessary
# hdi_data <- hdi_data %>%
# mutate(Date_Column = as.Date(Date_Column, format="%Y-%m-%d"))
# Check the updated structure after conversions
str(hdi_data)
We use mutate() and across() functions from
the dplyr package to convert the data types.
For numerical columns, if they are mistakenly read as factors or
characters, we convert them to numeric using as.numeric()
after ensuring they are characters with as.character().
This step is crucial to prevent the unintended conversion of factor
levels to their underlying integer codes.
For categorical data, we transform character columns to factors with
as.factor(), which facilitates various analyses, especially
in statistical modeling.
Date columns, if present, are converted from characters to Date objects
with as.Date(), applying the appropriate date format.
Post-conversion, we verify the dataframe’s structure with
str() to confirm the data types are correctly adjusted.
It’s noteworthy that converting characters to numeric will yield
NA for non-numeric strings. It is vital to convert only
those columns that are assuredly numeric values in string format.
Loading the further cleaned file, wherein every column name is in lowercase and without any spaces.
# Set the path to the CSV file
file_path <- "cleaned_HDI_data.csv"
# Read the CSV file into a dataframe
cleaned_hdi_data <- read.csv(file_path, header = TRUE, stringsAsFactors = FALSE)This plot will show the relationship between CO2 emissions per capita and the material footprint per capita for the latest available year (2021).
library(ggplot2)
library(plotly)
# Assuming 'cleaned_hdi_data' is your dataframe
p <- ggplot(cleaned_hdi_data, aes(x = carbondioxideemissionspercapitaproductiontonnes2021,
y = materialfootprintpercapitatonnes2021)) +
geom_point(aes(text = paste("Country:", factor_country)), size = 2) +
theme_minimal() +
labs(title = "CO2 Emissions vs. Material Footprint (2021)",
x = "CO2 Emissions Per Capita (tonnes)",
y = "Material Footprint Per Capita (tonnes)")
# Convert to interactive plot
p_interactive <- ggplotly(p, tooltip = "text")
p_interactiveThis plot will create a 3D scatter plot of life expectancy, education, and GNI per capita.
library(plotly)
# 3D scatter plot for life expectancy, education, and GNI per capita for the year 2021
p <- plot_ly(cleaned_hdi_data,
x = ~expectedyearsofschoolingmale2021,
y = ~lifeexpectancyatbirth2021,
z = ~grossnationalincomepercapita2021, # Updated with the correct column name
type = 'scatter3d',
mode = 'markers',
marker = list(size = 3),
text = ~country) # Updated with the correct column name for countries
pThis section will focus on creating an interactive bar chart that represents the Gross National Income (GNI) per capita for different countries in the year 2021.
library(plotly)
# Create a bar chart for GNI per capita for the year 2021
gni_bar_chart <- plot_ly(cleaned_hdi_data, x = ~country, y = ~grossnationalincomepercapita2021, type = 'bar', text = ~country)
gni_bar_chartThis plot is an interactive bar chart showing the Gross National Income (GNI) per capita for different countries in the year 2021. Each bar represents a country, and the height indicates the GNI per capita value.
This visualization will present an interactive line plot, illustrating the trends in life expectancy across various countries over time.
library(plotly)
library(dplyr)
library(tidyr)
# Pivot the life expectancy data to have a long format
life_expectancy_data <- cleaned_hdi_data %>%
select(country, starts_with("lifeexpectancyatbirth")) %>%
gather(key = "year", value = "life_expectancy", -country) %>%
mutate(year = as.numeric(gsub("lifeexpectancyatbirth", "", year)))
# Create the interactive line plot
life_expectancy_plot <- plot_ly(life_expectancy_data, x = ~year, y = ~life_expectancy, color = ~country, type = 'scatter', mode = 'lines')
life_expectancy_plotThis visualization shows the trend of life expectancy at birth for all countries over the available years. Each line represents a different country, and the user can hover over the plot to see the exact values.
This interactive visualization will plot the components of the Human Development Index (HDI) in a three-dimensional space, allowing for an immersive exploration of the data.
library(plotly)
# Create a 3D scatter plot with HDI components: Life Expectancy, Education, and GNI
hdi_3d_scatter <- plot_ly(cleaned_hdi_data,
x = ~lifeexpectancyatbirth2021,
y = ~meanyearsofschooling2021,
z = ~grossnationalincomepercapita2021,
color = ~humandevelopmentindex2021,
text = ~country,
type = 'scatter3d',
mode = 'markers')
hdi_3d_scatterThis plot creates a 3D scatter diagram that visualizes the three components of the Human Development Index (HDI): life expectancy at birth, mean years of schooling, and GNI per capita. The color of the markers represents the HDI value for 2021, offering a vivid depiction of each country’s development status.
The forthcoming interactive map will illustrate the Human Development Index (HDI) across countries, providing a geographical perspective on global human development levels.
The forthcoming interactive map will illustrate the Human Development Index (HDI) across countries, providing a geographical perspective on global human development levels.
library(plotly)
hdi_map <- plot_geo(cleaned_hdi_data, locationmode = 'ISO-3', locations = ~iso3, z = ~humandevelopmentindex2021, text = ~country, colors = "Blues")
hdi_mapThis visualization is an interactive choropleth map showing the Human Development Index by country for the year 2021. Countries are shaded based on their HDI value, providing a quick visual indication of human development across the globe. The map’s interactivity allows for engagement with the data, offering insights into the HDI of each country upon interaction.
How has the Human Development Index (HDI) of a specific country (or a set of countries) changed over the years? This question can be addressed using linear regression to model the trend of HDI over time.
library(shiny)
library(ggplot2)
library(dplyr)
library(tidyr)
hdi_data <- read.csv("cleaned_HDI_data.csv")
ui <- fluidPage(
titlePanel("HDI Trend Analysis"),
sidebarLayout(
sidebarPanel(
selectInput("countryInput",
"Select Country:",
choices = unique(hdi_data$country),
selected = "United States",
multiple = TRUE),
sliderInput("yearRange",
"Select Year Range:",
min = 1990,
max = 2021,
value = c(1990, 2021),
step = 1)
),
mainPanel(
plotOutput("hdiTrendPlot")
)
)
)
server <- function(input, output) {
output$hdiTrendPlot <- renderPlot({
filtered_data <- hdi_data %>%
filter(country %in% input$countryInput) %>%
select(c("country", paste0("humandevelopmentindex", 1990:2021))) %>%
pivot_longer(cols = starts_with("humandevelopmentindex"),
names_to = "year",
values_to = "hdi") %>%
mutate(year = as.numeric(sub("humandevelopmentindex", "", year))) %>%
filter(year >= input$yearRange[1] & year <= input$yearRange[2])
ggplot(filtered_data, aes(x = year, y = hdi, group = country, color = country)) +
geom_line() +
geom_point() +
theme_minimal() +
labs(title = "HDI Trend Over Time",
x = "Year",
y = "Human Development Index (HDI)") +
theme(legend.title = element_blank())
})
}
shinyApp(ui, server)This analysis was conducted with reference to the Shiny app available at https://shiny.posit.co/r/articles/share/shinyapps/, accessed on 21st November 2023.